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Background LUCADA Evaluation
Evaluating OWL 2 Reasoners in the context of Clinical Decision Support in Lung Cancer Treatment Selection
- M. Berkan Sesen
Background LUCADA Evaluation
Evaluating OWL 2 Reasoners in the context of Clinical Decision - - PowerPoint PPT Presentation
Evaluating OWL 2 Reasoners in the context of Clinical Decision - - PowerPoint PPT Presentation
Background LUCADA Evaluation Evaluating OWL 2 Reasoners in the context of Clinical Decision Support in Lung Cancer Treatment Selection M. Berkan Sesen Ernesto Jim enez-Ruiz Ren e Ba nares-Alc antara Sir Michael Brady Department
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Background LUCADA Evaluation
Background
Lung Cancer Treatment
- Lung cancer is responsible of the 21% of cancer-related
deaths.
- There are (substantial and unjustified) variations in treatment
decisions between cancer centres.
- Clinical guidelines (CGs) reduce variability in clinical practice.
- Originally CGs are unstructured and free-text documents, and
- ften not readily accessible at the point of decision making.
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Background LUCADA Evaluation
Background
Lung Cancer Treatment
- Lung cancer is responsible of the 21% of cancer-related
deaths.
- There are (substantial and unjustified) variations in treatment
decisions between cancer centres.
- Clinical guidelines (CGs) reduce variability in clinical practice.
- Originally CGs are unstructured and free-text documents, and
- ften not readily accessible at the point of decision making.
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Background LUCADA Evaluation
Background
Lung Cancer Treatment
- Lung cancer is responsible of the 21% of cancer-related
deaths.
- There are (substantial and unjustified) variations in treatment
decisions between cancer centres.
- Clinical guidelines (CGs) reduce variability in clinical practice.
- Originally CGs are unstructured and free-text documents, and
- ften not readily accessible at the point of decision making.
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Background LUCADA Evaluation
Background
Lung Cancer Treatment
- Lung cancer is responsible of the 21% of cancer-related
deaths.
- There are (substantial and unjustified) variations in treatment
decisions between cancer centres.
- Clinical guidelines (CGs) reduce variability in clinical practice.
- Originally CGs are unstructured and free-text documents, and
- ften not readily accessible at the point of decision making.
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Background LUCADA Evaluation
Background
Clinical decision support (CDS) systems can. . .
- facilitate the access to clinical guidelines.
- computerise CGs using structured logical languages.
- match guidelines rules against a patient record to infer the
appropiate treatment.
Examples
- PROforma. Fox et al. (1997)
- EON. Musen et al. (1996)
- GLIF3. Want et al. (2004)
- SAGE. Tu et al. (2007)
- LUNG CANCER ASSISTANT. Berkan Sesen et al. (2012)
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Background LUCADA Evaluation
Background
Clinical decision support (CDS) systems can. . .
- facilitate the access to clinical guidelines.
- computerise CGs using structured logical languages.
- match guidelines rules against a patient record to infer the
appropiate treatment.
Examples
- PROforma. Fox et al. (1997)
- EON. Musen et al. (1996)
- GLIF3. Want et al. (2004)
- SAGE. Tu et al. (2007)
- LUNG CANCER ASSISTANT. Berkan Sesen et al. (2012)
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Background LUCADA Evaluation
Background
Lung Cancer Assistant (LCA)
- An ontology-based system which provides guideline
rule-based decision support for lung cancer treatment.
- LCA exploits the English Lung Cancer Dataset (LUCADA)
LUCADA ontology
- LUCADA has been built using the OWL 2 language.
- Represents the semantic layer of the LCA:
- Captures the domain in the LUCADA dataset.
- Encodes the clinical guidelines.
- Represents patient data.
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Background LUCADA Evaluation
Background
Lung Cancer Assistant (LCA)
- An ontology-based system which provides guideline
rule-based decision support for lung cancer treatment.
- LCA exploits the English Lung Cancer Dataset (LUCADA)
LUCADA ontology
- LUCADA has been built using the OWL 2 language.
- Represents the semantic layer of the LCA:
- Captures the domain in the LUCADA dataset.
- Encodes the clinical guidelines.
- Represents patient data.
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Background LUCADA Evaluation
Outline
Background LUCADA Ontology Evaluation
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Background LUCADA Evaluation
LUCADA Ontology
Example of guideline rule
- Eligibility criteria are encoded as equivalence axioms.
- “Consider radiotherapy for Stage I, II, III patients with good
performance status” RT GR ≡ GoodPerformancePatient ⊓ ∃hasClinicalFinding. (NeoplasticDisease⊓ ∃hasPreHistology.NonsmallCellCarcinoma⊓ ∃hasPreTNMStaging.string⊓ ∀hasPreTNMStaging.{I, II, III})
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Background LUCADA Evaluation
LUCADA Ontology
Example of patient
- Each patient is encoded with ∼ 25 individual axioms.
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Background LUCADA Evaluation
LUCADA Ontology
Integration with SNOMED CT
- SNOMED is the reference ontology in the National Health
Service (NHS).
- To facilitate interoperability we have integrated LUCADA with
SNOMED.
- We have used LogMap matching system to
- identify the classes in SNOMED related to LUCADA.
- extract a lung cancer-specific module of SNOMED CT.
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Background LUCADA Evaluation
LUCADA Ontology
Summary of LUCADA and LUCADA-SNOMED metrics
Metric Ontology LUCADA-SNOMED LUCADA DL Expressivity ALCHIF(D) ALCHI(D) # Classes 1553 376 # Object properties 63 37 # Data Properties 63 63 # Equiv. class axioms 1050 40 # Subclass of axioms 999 386 # Prop. domain axioms 97 97 # Prop. range axioms 30 30
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Background LUCADA Evaluation
Outline
Background LUCADA Ontology Evaluation
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Background LUCADA Evaluation
Evaluation
Evaluation settings
- Windows 7 64-bit desktop computer,
- 15 GiB of RAM, and
- Intel Xeon 2.27 GHz CPU.
- Results have been calculated as average of at least 10
repetitions of the experiment.
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Background LUCADA Evaluation
Evaluation
Evaluated Reasoners
- HermiT 1.3.7, Pellet 2.3.0 and FaCT++ 1.6.2
Experiments
- Increasing the TBox with guideline rules or patient scenarios.
- Increasing the ABox with patient records.
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Background LUCADA Evaluation
Evaluation
Experiment 1: Increasing the TBox with guideline rules
- 1 to 40 patient scenarios or guideline rules.
- With LUCADA and LUCADA-SNOMED with 1 patient.
- Recorded times for classification and realisation.
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Background LUCADA Evaluation
Experiment 1 (increasing TBox) with LUCADA
50 100 150 200 250 5 10 15 20 25 30 35 40 50 100 150 200 250 Time (ms) Number of patient scenarios FaCT++ (total) Pellet (total) HermiT (classification) HermiT (realisation)
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Background LUCADA Evaluation
Experiment 1 (increasing TBox) with LUCADA-SNOMED
1000 3000 5000 7000 9000 40000 50000 5 10 15 20 25 30 35 40 1000 3000 5000 7000 9000 40000 50000 Time (ms) Number of patient scenarios FaCT++ (total) Pellet (total) HermiT (classification) HermiT (realisation)
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Background LUCADA Evaluation
Evaluation
Experiment 2: Increasing the ABox with patient records
- 1 to 100 patient records.
- Experiment with LUCADA with 40 patient scenarios.
- Recorded times for realisation of all patients.
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Background LUCADA Evaluation
Experiment 2 (increasing ABox) with LUCADA
10000 20000 30000 40000 50000 60000 70000 20 40 60 80 100 10000 20000 30000 40000 50000 60000 70000 Time (ms) Number of patients FaCT++ Pellet HermiT
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Conclusions from the LCA experiments
- FaCT++ is currently the best choice for LCA.
- HermiT provides the fastest TBox reasoning for
LUCADA-SNOMED CT.
- HermiT does not scale for ABOX reasoning with LUCADA.
- Pellet performs well in classifying the LUCADA.
- Pellet struggles with the LUCADA-SNOMED CT ontology.
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Questions?
- Lung Cancer Assistant (LCA):
http://lca.eng.ox.ac.uk/LungCancerSmartGWT/
- LCA’s main contact:
Berkan Sesen (berkan.sesen@eng.ox.ac.uk)
- Tests and LUCADA-SNOMED integration:
Ernesto Jimenez Ruiz (ernesto.jimenez.ruiz@gmail.com)
Thank you for your attention Acknowledgements
- The LCA project was funded by the CDT in Healthcare